from PIL import Image, ImageEnhance
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import pandas as pd
import math

import requests
import json
import re
import csv

directory_path = os.getcwd()
parent_directory_path = os.path.dirname(directory_path)
csv_path = os.path.join(parent_directory_path, 'Model\\condo_data_new_FINAL_test.csv')
gt_masked_image_path = os.path.join(parent_directory_path, 'Model\\buildings\\test')
generated_image_path = os.path.join(parent_directory_path, 'Model\\buildings\\final_buildings_output_2') 

# Read the CSV file
data = pd.read_csv(csv_path)

# Function to extract the numeric part of the filename
def extract_numeric_part(filename):
    numeric_part = ''.join(filter(str.isdigit, filename))
    return int(numeric_part) if numeric_part else None

def create_binary_mask(arr, target_color, threshold=30):
    lower_bound = np.array(target_color) - threshold
    upper_bound = np.array(target_color) + threshold
    mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
    return np.all(mask, axis=-1)

def extract_building_regions(arr, target_color, threshold=10):
    lower_bound = np.array(target_color) - threshold
    upper_bound = np.array(target_color) + threshold
    mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
    return np.all(mask, axis=-1)

# def find_max_building_storeys(gpr):
#     max_building_storeys= 0
#     if gpr >= 0 and gpr < 1.4:
#         max_building_storeys = 5
#     elif gpr >= 1.4 and gpr < 1.6:
#         max_building_storeys = 12
#     elif gpr >= 1.6 and gpr < 2.1:
#         max_building_storeys = 24
#     elif gpr >= 2.1 and gpr < 2.8:
#         max_building_storeys = 36
#     elif gpr >= 2.8:
#         max_building_storeys = 48 ## by right got no limit
#     return max_building_storeys

def masked_rgb(simp_gpr):
    rgb = [0,0,0]
    if simp_gpr == 1.4:
        rgb = [255, 10, 169]
    elif simp_gpr == 1.6:
        rgb = [200,130,60]
    elif simp_gpr == 2.1:
        rgb = [0,0,255]
    elif simp_gpr == 2.8:
        rgb = [255,0,0]
    elif simp_gpr == 3.0:
        rgb =[0,0,0]
    return rgb

'''
pink, [255, 10, 169]
brown, [200,130,60]
cyan, [0,255,255]
red, [255,0,0]
black, [0,0,0]
green, [0,255,0]
blue, [0,0,255]
yellow, [255, 255, 0]
'''

# absolute_accuracies = []
# losses =[]
# images =[]
# sanity_ratios =[]

gprs =[]
generated_gprs =[]
sanity_ratios =[]

# Iterate through the images in the generated_image_path
for image_file in os.listdir(generated_image_path):
    if image_file.endswith('.png'):
        image_index = extract_numeric_part(image_file)

        # Construct the path for the corresponding masked image
        gt_mask_image_filename = f"{image_index}.png"
        gt_mask_image = os.path.join(gt_masked_image_path, gt_mask_image_filename)
        open_gt_mask_image = Image.open(gt_mask_image)
        mask_crop_box = (512, 0, 1024, 512) # right side
        mask_image = open_gt_mask_image.crop(mask_crop_box) #gt_mask is concatenated gt and mask
        gt_crop_box = (0, 0, 512, 512) # left side
        gt_image = open_gt_mask_image.crop(gt_crop_box)

        generated_image = os.path.join(generated_image_path, image_file)
        generated_image =  Image.open(generated_image)

        # Check if the image index matches any index in the CSV
        matched_row = data[data['key1'] == image_index]
        if not matched_row.empty:
            # Extract the GPR value for the matched row
            gpr_value = matched_row['GPR'].iloc[0]
            storey = matched_row['storeys'].iloc[0]
            simplified_gpr_value = matched_row['simp_gpr'].iloc[0]
            actual_site_area = matched_row['area'].iloc[0]
            actual_site_area = actual_site_area.replace(',', '')
            actual_site_area = float(actual_site_area[:-4])
            gpr_value = float(gpr_value)
            storey = int(storey)
            mask_array = np.array(mask_image)
            generated_array = np.array(generated_image)

            mask_color = [0,255,0] # green
            site_mask = create_binary_mask(mask_array, mask_color)
            site_area_array = generated_array.copy()
            site_area_array[~site_mask] = [255, 255, 255, 255] # making non-masked region white RMB ITS 4 CHANNELS NOW
            site_area_image = Image.fromarray(site_area_array)

            mask_color = masked_rgb(simplified_gpr_value)
            building_mask = extract_building_regions(site_area_array, mask_color)
            buildings_image = Image.fromarray(building_mask)

            plt.figure(figsize=(20, 5))
            plt.subplot(1, 4, 1)
            plt.imshow(mask_image)
            plt.title('Mask Image')
            plt.axis('off')
            plt.subplot(1, 4, 2)
            plt.imshow(gt_image)
            plt.title('GT Image')
            plt.axis('off')
            plt.subplot(1, 4, 3)
            plt.imshow(generated_image)
            plt.title('Generated Image')
            plt.axis('off')
            plt.subplot(1, 4, 4)
            plt.imshow(buildings_image, cmap='gray')
            plt.title('Buildings Image')
            plt.axis('off')
            plt.show()

            building_pixels = np.sum(building_mask)
            mask_pixels = np.sum(site_mask)
            msq_per_pixel = actual_site_area/mask_pixels
            building_area = msq_per_pixel * building_pixels
            #max_storeys = find_max_building_storeys(gpr_value)
            generated_gpr = building_area*storey/actual_site_area
            gprs.append(gpr_value)
            generated_gprs.append(generated_gpr)

            print(f'Image: {image_file}, GPR: {gpr_value}, Simplified GPR: {simplified_gpr_value}, Storeys:{storey},  Site area: {actual_site_area}, Building pixels: {building_pixels}, Mask pixels: {mask_pixels}, Generated GPR: {generated_gpr}')

            #sanity check. ratios should be about 0.75
            ratio = mask_pixels/actual_site_area
            sanity_ratios.append(ratio)



total_data = len(gprs)
accuracies = []
absolute_error =[]
square_error =[]
for tar_gpr, gen_gpr in zip(gprs, generated_gprs):
    accuracies.append(abs((tar_gpr-gen_gpr)/tar_gpr))
    absolute_error.append(abs(tar_gpr-gen_gpr))
    square_error.append((tar_gpr-gen_gpr)**2)
accuracy = sum(accuracies)/total_data
mean_abs_error = sum(absolute_error)/total_data
root_squared_error = math.sqrt(sum(square_error)/total_data)
print(f"Accuracies:{accuracies} \nSquare error:{square_error} \nAbsolute error:{absolute_error} ")
print(f"\nAccuracy:{accuracy} MAE:{mean_abs_error} RMSE:{root_squared_error}")

Image: 1040.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5,  Site area: 23065.1, Building pixels: 1018, Mask pixels: 16203, Generated GPR: 0.31413935690921435

Image: 1074.png, GPR: 2.5, Simplified GPR: 2.8, Storeys:12,  Site area: 37265.0, Building pixels: 3101, Mask pixels: 27439, Generated GPR: 1.3561718721527753

Image: 1076.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:36,  Site area: 10414.2, Building pixels: 819, Mask pixels: 8554, Generated GPR: 3.4468085106382977

Image: 1102.png, GPR: 1.6, Simplified GPR: 1.6, Storeys:12,  Site area: 6157.3, Building pixels: 8, Mask pixels: 4778, Generated GPR: 0.020092088740058602

Image: 1180.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:15,  Site area: 19547.0, Building pixels: 1776, Mask pixels: 14355, Generated GPR: 1.8557993730407525

Image: 1379.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5,  Site area: 17455.9, Building pixels: 1912, Mask pixels: 12216, Generated GPR: 0.7825802226588081

Image: 145.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:15,  Site area: 22094.4, Building pixels: 1630, Mask pixels: 16292, Generated GPR: 1.500736557819789

Image: 1484.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17,  Site area: 10097.1, Building pixels: 1874, Mask pixels: 7670, Generated GPR: 4.1535853976531945

Image: 1602.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17,  Site area: 13564.8, Building pixels: 2080, Mask pixels: 9962, Generated GPR: 3.5494880546075085

Image: 1655.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:18,  Site area: 27418.2, Building pixels: 2663, Mask pixels: 21829, Generated GPR: 2.195886206422649